| Full text | |
| Author(s): |
Scardua‚ L.A.
;
Da Cruz‚ J.J.
;
Reali Costa‚ A.H.
Total Authors: 3
|
| Document type: | Journal article |
| Source: | ADVANCED ENGINEERING INFORMATICS; v. 16, n. 3, p. 217-227, 2002. |
| Abstract | |
This paper describes the use of Reinforcement Learning (RL) to the computation of time-optimal anti-swing control of a ship unloader. The unloading cycle has been divided into six subtasks and an optimization problem has been defined for each of them. A RL algorithm together with a multilayer perceptron neural network as a value function approximator have been used in the optimization. The results obtained are encouraging, since they reproduce a solution previously generated by using Optimal Control Theory. (C) 2003 Elsevier Science Ltd. All rights reserved. (AU) | |
| FAPESP's process: | 97/04668-1 - Control of systems with dynamic subject to uncertainties |
| Grantee: | Oswaldo Luiz Do Valle Costa |
| Support Opportunities: | Research Projects - Thematic Grants |